“The problem is that NDVI is just a qualitative measurement of
vegetation density, but it is not a quantitative measure of how much vegetation
is present,” says Buermann. “Many scientists don’t trust NDVI
measurements enough to use them in climate models.” In other words, NDVI
can give researchers an idea of how lush the vegetation is in one area of the
world relative to another, but it cannot tell them quantitatively how much
vegetation there is in that one spot. Only by observing NDVI in an area over a
long period of time and comparing it to other regions around the world, can
researchers get a fix on what the normal vegetation density is for a region.
Many in the science community feel that NDVI values are too inexact to use in
climate models to determine, for instance, how much carbon plants draw down each
year in a given area.

Buermann explains that there is a better measurement for plant density and
growth known as leaf area index (LAI). LAI assigns a quantifiable value to the
amount of vegetation on the ground. Simply put, LAI is the leaf area per unit
ground area as seen when looking down on vegetation. One can imagine looking
down on a tree canopy from a platform that stands high above the treetops. A
tree canopy would have a leaf area index of one if every square inch of the
ground below the tree canopy were overshadowed by exactly one leaf in the tree
canopy. If exactly two leaves blocked the view of the ground then the tree
canopy would have a leaf area index of two. Of course, most trees have layers
and layers of leaves obstructing patches of the land unevenly. So a broadleaf
deciduous forest (one that loses its leaves in the fall) typically will have a
leaf area index of 3 or above in the summer, and evergreen conifer trees will
have an LAI range of between 2 and 3.5 year round.

Horticulturists and biologists have used leaf area index to measure leaf
density and vegetation health since the early part of the last century.
Measurements were usually made on a local scale on the ground or from an
airplane. For roughly the last decade, remote sensing scientists have attempted
to measure LAI on a global scale using satellite data. The difficulty has always
been one of accurately calculating the amount of leaves on millions of trees and
shrubs from images taken from many miles above the ground. Previous attempts
have yielded only very rough measurements of LAI directly from NDVI values.

Buermann says he and his colleagues have uncovered a way to obtain more
accurate values. The Boston team has developed a computer program that takes
satellite data and other information gathered about the Earth’s surface
and transforms them into values for leaf area index. “Essentially, what we
try to do is simulate how the light is reflected off of the vegetation
canopy,” says Buermann. Given ground cover and soil type, their computer
simulation calculates what light from the sun would look like after it hits the
vegetation and the ground and is reflected back up through the leaves, the
atmosphere, and into space. Using this computer simulation, the scientists can
compute LAI values by observing near-infrared and visible light from satellite
data.

Together with researchers form the University of Arizona, Georgia Tech, and
Ames Research Center, the scientists first employed their method on the AVHRR
satellite data gathered from 1981 to 1990. They created global data sets/maps
that displayed the average LAI values over the globe for each month over the
ten-year period derived from the infrared and visible light readings from the
data. “We then needed to show that LAI computed from satellite data are
consistent with observed data,” says Buermann. Where they could, the
researchers compared the satellite LAI values to existing LAI values obtained by
ground-based measurements over the same period. Most of these
records were of farmland in the Midwestern Plains States and of temperate and
boreal forests of North America. The LAI values matched up well for both types
of terrain (Buermann et al. 2001a).

Leaf Area Index (LAI) is
related to, but not directly proportional to, Normalized Difference
Vegetation Index (NDVI). In addition, different vegetation types (broadleaf evergreens
versus needleleaf evergreens, for example) and soil types exhibit different
relationships between the two parameters. The graph at left compares NDVI to LAI.
Unfortunately, the relationship between NDVI and LAI is
not unique (multiple values of NDVI correspond to a single LAI value), a problem which is being addressed by the current generation of satellite
instruments, such as the Moderate-Resolution Imaging Spectroradiometer (MODIS). (Graph courtesy
Wolfgang Buermann, Boston University Climate and
Vegetation Research Group.)

Unfortunately, these ground-based records weren’t very extensive. To
test their LAI values thoroughly in this manner, the researchers would require
LAI measurements for all types of terrain and all regions of the world. But
conducting ground-based surveys of leaf area index worldwide would have been
incredibly costly and time consuming. As an alternative, the team compared LAI
values to known changes in plant growth around the world. If the LAI values were
representative of vegetation density, then they should move in concert with the
changes in seasons, geography, and rainfall patterns.

Buermann and his colleagues went about verifying, for instance, that LAI
values of broadleaf evergreen trees in equatorial rainforests resulted in some
of the highest LAI values and barren deserts resulted in some of the lowest
values. They verified that seasonal changes in vegetation in the northern
latitudes resulted in smooth seasonal changes in LAI. They even compared the LAI
measurements to El Niño events in the 1980s. As expected, in areas that
experience greater rainfall during El Niño, such as the west coast of Central
America and South America, LAI values were higher. In areas that experience less
rainfall, such as Australia, the LAI values were lower (Buermann et al. 2001a).
“We had good agreement in semi-arid, tropical, and subtropical areas where
change in vegetation and precipitation run together,” says Buermann.

Scientists used the long record of
NDVI data acquired by NOAA’s
polar-orbiting weather satellites to create a long-term LAI
dataset. The image at left shows LAI values from March 1991. An animation shows average monthly LAI from July 1981 to June
1991. The MODIS instruments
aboard the Terra and Aqua satellites will extend this dataset with more accurate LAI
measurements. (Data provided by Boston University Climate and
Vegetation Research Group. Image and animation by Robert
Simmon)